Acquirement of Rituximab Resistance in Lymphoma Cell Lines Is Associated with Both Global<i>CD20</i>Gene and Protein Down-Regulation Regulated at the Pretranscriptional and Posttranscriptional Levels
Bibliographic record
Abstract
Acquirement of resistance to rituximab has been observed in lymphoma patients. To define mechanisms associated with rituximab resistance, we developed various rituximab-resistant cell lines (RRCL) and studied changes in CD20 expression/structure, lipid raft domain (LRD) reorganization, calcium mobilization, antibody-dependent cellular cytotoxicity, and complement-mediated cytotoxicity (CMC) between parental and RRCL. Significant changes in surface CD20 antigen expression were shown in RRCL. Decreased calcium mobilization and redistribution of CD20 into LRD were found in RRCL. Western blotting identified a unique 35 kDa protein band in RRCL, which was not seen in parental cells and was secondary to an increase in surface and cytoplasmic expression of IgM light chains. CD20 gene expression was decreased in RRCL. In vitro exposure to PS341 increased CD20 expression in RRCL and minimally improved the sensitivity to rituximab-associated CMC. Our data strongly suggest that the acquisition of rituximab resistance is associated with global gene and protein down-regulation of the CD20 antigen affecting LRD organization and downstream signaling. CD20 expression seems to be regulated at the pretranscriptional and posttranscriptional levels. Proteasome inhibition partially reversed rituximab resistance, suggesting the existence of additional mediators of rituximab resistance. Future research is geared to identify drugs and/or biological agents that are effective against RRCL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".